A Cognitive Governance Framework for AI-Enabled Enterprise Transformation and Secure Cloud Operations

Main Article Content

Trisha Gee

Abstract

The rapid adoption of Artificial Intelligence (AI) and cloud computing technologies has transformed modern enterprises by enhancing operational efficiency, decision-making capabilities, and digital innovation. However, the integration of AI-driven systems into enterprise environments introduces significant governance, security, compliance, and ethical challenges. This study proposes a Cognitive Governance Framework (CGF) designed to support AI-enabled enterprise transformation while ensuring secure cloud operations. The framework integrates cognitive intelligence, risk management, regulatory compliance, cybersecurity controls, and continuous monitoring mechanisms into a unified governance architecture. By leveraging machine learning, predictive analytics, automated policy enforcement, and cloud-native security practices, organizations can achieve greater transparency, accountability, and resilience in digital ecosystems. The proposed framework emphasizes adaptive decision-making, real-time threat detection, data governance, and strategic alignment between business objectives and technological innovation. Furthermore, it addresses emerging concerns related to AI ethics, privacy protection, algorithmic bias, and cloud security vulnerabilities. The research highlights how cognitive governance can facilitate sustainable enterprise transformation by balancing innovation with security and compliance requirements. The study contributes to both academic and practical domains by offering a comprehensive governance model capable of supporting intelligent, secure, and scalable enterprise operations in increasingly complex cloud-based environments.

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How to Cite

A Cognitive Governance Framework for AI-Enabled Enterprise Transformation and Secure Cloud Operations. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8040-8048. https://doi.org/10.15662/IJRPETM.2023.0601005

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